By Lydia | Last Updated
Last year, ChatGPT emerged and continues to be highly popular. The rapid development of artificial intelligence heavily relies on high-performance, high-computing hardware, with GPUs playing a crucial role in ChatGPT.
While initially designed for graphics and image processing, GPUs' parallel computing capabilities enable efficient handling of large language models, making them an ideal choice for processing massive neural networks and for deep learning models. But what exactly is a GPU, and how does it differ from a CPU? This article will answer these questions in detail.
A GPU (Graphics Processing Unit), also known as a graphics core, visual processor, or graphics chip, specializes in handling a large number of simple tasks, including graphics and video rendering.
Sometimes we refer to the GPU as a graphics card, but there is a subtle difference: the GPU refers to the chip responsible for processing various tasks, while the graphics card refers to the board that combines the GPU chip, video memory, interfaces, etc.
GPUs are categorized based on their system integration into two types: Integrated GPUs (iGPU) and Discrete GPUs (dGPU). The main differences between them are illustrated below.
GPU is commonly described as generating 3D graphics, mapping graphics to corresponding pixels, calculating the final color for each pixel, and completing the output.
The workflow of how a GPU operates is as follows:
1. The GPU consists of multiple cores, each capable of independently executing commands.
2. Each core has its own memory for storing instructions and data.
3. The GPU receives instructions from the CPU and distributes them to multiple cores for processing.
4. Processed data is then transferred back to the CPU for further use.
5. The performance of a GPU depends on factors such as the number of cores, clock frequency, and RAM capacity it possesses.
Modern GPUs have two main functions: serving as powerful graphics engines and acting as highly parallel programmable processors for various neural network or machine learning tasks.
Graphics GPUs are widely used in gaming, image processing, and cryptocurrency mining, focusing on parameters such as frame rates, rendering realism, and realistic scene mapping. General-purpose GPUs are primarily applied in large-scale artificial intelligence computing, data centers, supercomputing, and other scenarios to support larger data volumes and concurrent throughput.
CPUs are primarily used to handle a wide variety of computing tasks, from running operating systems and applications to executing complex computational tasks. They excel at handling a small number of complex tasks with strong single-core performance. Additionally, CPUs contain sophisticated control logic, enabling them to efficiently execute diverse instruction sets.
On the other hand, GPUs were originally designed for graphics rendering, particularly 3D graphics processing, but are now widely used for parallel processing tasks such as deep learning and scientific computation. GPUs comprise a large number of simple cores optimized for handling massive parallel computing tasks. They can simultaneously process thousands of simple computational tasks.
In short, CPUs excel at managing complex, overarching operations, while GPUs are adept at performing simple, repetitive operations on large datasets. So, GPU is more capable for deep learning. More difference between a CPU and GPU are as follows:
CPU: Best suited for multitasking and complex logical control tasks, such as running operating systems, application logic, and general computing tasks.
GPU: Particularly well-suited for handling large-scale parallel tasks such as image rendering, cryptographic calculations, and machine learning training.
CPU is suitable for tasks requiring high single-thread performance and complex control logic, while GPU excels in graphics processing and data-intensive tasks (such as deep learning). However, there is no better CPU or better GPU, because GPU and CPU have their respective areas of expertise. And GPU cannot function independently, as it relys on CPU control and invocation.
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